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Comments on Primary Papers and News

Comment by Randy L. Buckner and Cindy Lustig
A major challenge to developing therapies for Alzheimer's disease is the availability of valid and robust diagnostic markers. Clinical assessment and cognitive testing have traditionally been the gold standard. Over the past decade, there has been an increasing emphasis on two categories of neuroimaging markers—those based on structural measures, and those based on metabolic measures. Greicius and colleagues, in their recent paper in the Proceedings of the National Academy of Sciences (2004), suggest a novel diagnostic marker for Alzheimer's disease, based on functional MRI measures.

Their work is based on the recent discovery of a "default network" that is ubiquitously observed in brain imaging studies of healthy, young participants (Raichle et al., 2001). Default network activity is observed during periods of rest and passive tasks that do not require targeted, effortful processing. Anticipating the work of Greicius and colleagues, it is noteworthy that default network in young adults, which prominently includes regions in posterior cingulate and lateral parietal cortex, overlaps anatomically with those regions showing metabolic differences in Alzheimer's disease measured with FDG PET.

Employing a sophisticated analytic procedure that explores brain activity across networks of regions, Greicius et al. optimized the identification of the default network in elderly individuals with and without the earliest signs of Alzheimer's disease. Sensitivity and specificity of discrimination were 85 percent and 77 percent, respectively. As noted in the news story by Hakon Heimer, these numbers are promising and in the range considered clinically relevant.

Greicius and colleagues' observations are important from the perspectives of both clinical and basic science. First, the demonstrated discrimination between demented and nondemented groups using this functional MRI measure holds promise for developing a novel biomarker of Alzheimer's disease that may complement FDG PET metabolic measures. The relation between the changes in default network activity reported here and the common metabolic changes typically measured using FDG PET requires further exploration, but the possibility that the two are strongly related and that functional MRI measures may provide a complementary assessment in the early stages of dementia is intriguing.

Second, the results of their network analysis suggest a functional link to the medial temporal lobe structures that show early pathology in Alzheimer's disease. This may help to resolve the longstanding puzzle of how the pathological changes in medial temporal regions relate to the metabolic changes in parietal and posterior cingulate cortex as measured by PET. The Greicius et al. data suggest the tentative possibility that they are functionally linked and that posterior cortical changes, particularly within posterior cingulate cortex, may arise from anatomic projections between the medial temporal lobe and these regions.

In addition to the specific findings of their study, a social-scientific milestone was achieved in their paper. The data used for their discoveries were not their own. The data were downloaded from a freely available, online archive of raw functional imaging data of previously published manuscripts. The original authors who collected the data had not conceived of the form of analysis Greicius would later employ (we can speak to this point firsthand as original authors of the data). Thus, as intriguing as the results is also the process by which the discovery was made. The work of Greicius and colleagues directly illustrates the potential of open data sharing.